guilhem_lavaux/JaxPM
0
0
Fork 1
mirror of https://github.com/DifferentiableUniverseInitiative/JaxPM.git synced 2025-04-07 12:20:54 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

format

Browse source
This commit is contained in:
Wassim KABALAN 2024-10-22 12:57:34 -04:00
parent 86233081e2
commit 82b8f563a0
4 changed files with 60 additions and 44 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

4
benchmarks/particle_mesh.slurm
View file

@ -1,6 +1,6 @@
#!/bin/bash
##############################################################################################################################
# USAGE:sbatch --account=tkc@a100 --nodes=1 --gres=gpu:1 --tasks-per-node=1 -C a100 benchmarks/particle_mesh_a100.slurm
# USAGE:sbatch --account=tkc@a100 --nodes=1 --gres=gpu:1 --tasks-per-node=1 -C a100 benchmarks/particle_mesh_a100.slurm
##############################################################################################################################
#SBATCH --job-name=Particle-Mesh # nom du job
#SBATCH --cpus-per-task=8 # nombre de CPU par tache pour gpu_p5 (1/8 du noeud 8-GPU)
@ -140,7 +140,7 @@ fi
# GPU name is a100 if num_gpu_per_node is 8, otherwise it is v100
out_dir="pm_prof/$gpu_name/$nb_gpus"
trace_dir="traces/$gpu_name/$nb_gpus/bench_pm"
echo "Output dir is : $out_dir"
echo "Output dir is : $out_dir"
echo "Trace dir is : $trace_dir"
for pr in "${precisions[@]}"; do

4
benchmarks/pmwd_pm.slurm
View file

@ -1,6 +1,6 @@
#!/bin/bash
##############################################################################################################################
# USAGE:sbatch --account=tkc@a100 --nodes=1 --gres=gpu:1 --tasks-per-node=1 -C a100 benchmarks/particle_mesh_a100.slurm
# USAGE:sbatch --account=tkc@a100 --nodes=1 --gres=gpu:1 --tasks-per-node=1 -C a100 benchmarks/particle_mesh_a100.slurm
##############################################################################################################################
#SBATCH --job-name=Particle-Mesh # nom du job
#SBATCH --cpus-per-task=8 # nombre de CPU par tache pour gpu_p5 (1/8 du noeud 8-GPU)
@ -126,7 +126,7 @@ fi
out_dir="pm_prof/$gpu_name/$nb_gpus"
trace_dir="traces/$gpu_name/$nb_gpus/bench_pmwd"
echo "Output dir is : $out_dir"
echo "Output dir is : $out_dir"
echo "Trace dir is : $trace_dir"
for pr in "${precisions[@]}"; do

12
jaxpm/kernels.py
View file

@ -43,7 +43,7 @@ def interpolate_power_spectrum(input, k, pk, sharding=None):
def gradient_kernel(kvec, direction, order=1):
"""
Computes the gradient kernel in the requested direction
Parameters
-----------
kvec: list
@ -84,8 +84,8 @@ def invlaplace_kernel(kvec):
Complex kernel values
"""
kk = sum(ki**2 for ki in kvec)
kk_nozeros = jnp.where(kk==0, 1, kk)
return - jnp.where(kk==0, 0, 1 / kk_nozeros)
kk_nozeros = jnp.where(kk == 0, 1, kk)
return -jnp.where(kk == 0, 0, 1 / kk_nozeros)
def longrange_kernel(kvec, r_split):
@ -98,12 +98,12 @@ def longrange_kernel(kvec, r_split):
List of wave-vectors
r_split: float
Splitting radius
Returns
--------
wts: array
Complex kernel values
TODO: @modichirag add documentation
"""
if r_split != 0:
@ -124,7 +124,7 @@ def cic_compensation(kvec):
-----------
kvec: list
List of wave-vectors
Returns:
--------
wts: array

84
jaxpm/pm.py
View file

@ -9,8 +9,8 @@ from jaxpm.distributed import (autoshmap, fft3d, get_local_shape, ifft3d,
normal_field)
from jaxpm.growth import (dGf2a, dGfa, growth_factor, growth_factor_second,
growth_rate, growth_rate_second)
from jaxpm.kernels import (PGD_kernel, fftk, gradient_kernel, invlaplace_kernel,
longrange_kernel)
from jaxpm.kernels import (PGD_kernel, fftk, gradient_kernel,
invlaplace_kernel, longrange_kernel)
from jaxpm.painting import cic_paint, cic_paint_dx, cic_read, cic_read_dx
@ -38,11 +38,11 @@ def pm_forces(positions,
kvec = fftk(delta_k)
# Computes gravitational potential
pot_k = delta_k * invlaplace_kernel(kvec) * longrange_kernel(kvec,
r_split=r_split)
pot_k = delta_k * invlaplace_kernel(kvec) * longrange_kernel(
kvec, r_split=r_split)
# Computes gravitational forces
forces = jnp.stack([
cic_read_dx(ifft3d( - gradient_kernel(kvec, i) * pot_k),
cic_read_dx(ifft3d(-gradient_kernel(kvec, i) * pot_k),
halo_size=halo_size,
sharding=sharding) for i in range(3)
],
@ -51,9 +51,9 @@ def pm_forces(positions,
return forces
def lpt(cosmo, initial_conditions, a, halo_size=0, sharding=None,order=1):
def lpt(cosmo, initial_conditions, a, halo_size=0, sharding=None, order=1):
"""
Computes first and second order LPT displacement and momentum,
Computes first and second order LPT displacement and momentum,
e.g. Eq. 2 and 3 [Jenkins2010](https://arxiv.org/pdf/0910.0258)
"""
gpu_mesh = sharding.mesh if sharding is not None else None
@ -68,7 +68,7 @@ def lpt(cosmo, initial_conditions, a, halo_size=0, sharding=None,order=1):
a = jnp.atleast_1d(a)
E = jnp.sqrt(jc.background.Esqr(cosmo, a))
E = jnp.sqrt(jc.background.Esqr(cosmo, a))
delta_k = fft3d(initial_conditions)
initial_force = pm_forces(displacement,
delta=delta_k,
@ -76,7 +76,7 @@ def lpt(cosmo, initial_conditions, a, halo_size=0, sharding=None,order=1):
sharding=sharding)
dx = growth_factor(cosmo, a) * initial_force
p = a**2 * growth_rate(cosmo, a) * E * dx
f = a**2 * E * dGfa(cosmo,a) * initial_force
f = a**2 * E * dGfa(cosmo, a) * initial_force
if order == 2:
kvec = fftk(delta_k)
pot_k = delta_k * invlaplace_kernel(kvec)
@ -89,26 +89,30 @@ def lpt(cosmo, initial_conditions, a, halo_size=0, sharding=None,order=1):
# shear_ii = jnp.fft.irfftn(- ki**2 * pot_k)
nabla_i_nabla_i = gradient_kernel(kvec, i)**2
shear_ii = jnp.fft.irfftn(nabla_i_nabla_i * pot_k)
delta2 += shear_ii * shear_acc
delta2 += shear_ii * shear_acc
shear_acc += shear_ii
# for kj in kvec[i+1:]:
for j in range(i+1, 3):
for j in range(i + 1, 3):
# Substract squared strict-up-triangle terms
# delta2 -= jnp.fft.irfftn(- ki * kj * pot_k)**2
nabla_i_nabla_j = gradient_kernel(kvec, i) * gradient_kernel(kvec, j)
nabla_i_nabla_j = gradient_kernel(kvec, i) * gradient_kernel(
kvec, j)
delta2 -= jnp.fft.irfftn(nabla_i_nabla_j * pot_k)**2
delta_k2 = fft3d(delta2)
init_force2 = pm_forces(displacement, delta=delta_k2,halo_size=halo_size,sharding=sharding)
init_force2 = pm_forces(displacement,
delta=delta_k2,
halo_size=halo_size,
sharding=sharding)
# NOTE: growth_factor_second is renormalized: - D2 = 3/7 * growth_factor_second
dx2 = 3/7 * growth_factor_second(cosmo, a) * init_force2
dx2 = 3 / 7 * growth_factor_second(cosmo, a) * init_force2
p2 = a**2 * growth_rate_second(cosmo, a) * E * dx2
f2 = a**2 * E * dGf2a(cosmo, a) * init_force2
dx += dx2
p += p2
f += f2
p += p2
f += f2
return dx, p, f
@ -153,6 +157,7 @@ def make_ode_fn(mesh_shape, halo_size=0, sharding=None):
return nbody_ode
def get_ode_fn(cosmo, mesh_shape, halo_size=0, sharding=None):
def nbody_ode(a, state, args):
@ -162,11 +167,13 @@ def get_ode_fn(cosmo, mesh_shape, halo_size=0, sharding=None):
Compatible with [Diffrax API](https://docs.kidger.site/diffrax/)
"""
pos, vel = state
forces = pm_forces(pos, mesh_shape, halo_size=halo_size, sharding=sharding) * 1.5 * cosmo.Omega_m
forces = pm_forces(
pos, mesh_shape, halo_size=halo_size,
sharding=sharding) * 1.5 * cosmo.Omega_m
# Computes the update of position (drift)
dpos = 1. / (a**3 * jnp.sqrt(jc.background.Esqr(cosmo, a))) * vel
# Computes the update of velocity (kick)
dvel = 1. / (a**2 * jnp.sqrt(jc.background.Esqr(cosmo, a))) * forces
@ -177,7 +184,7 @@ def get_ode_fn(cosmo, mesh_shape, halo_size=0, sharding=None):
def pgd_correction(pos, mesh_shape, params):
"""
improve the short-range interactions of PM-Nbody simulations with potential gradient descent method,
improve the short-range interactions of PM-Nbody simulations with potential gradient descent method,
based on https://arxiv.org/abs/1804.00671
args:
@ -188,20 +195,24 @@ def pgd_correction(pos, mesh_shape, params):
delta = cic_paint(jnp.zeros(mesh_shape), pos)
alpha, kl, ks = params
delta_k = jnp.fft.rfftn(delta)
PGD_range=PGD_kernel(kvec, kl, ks)
pot_k_pgd=(delta_k * invlaplace_kernel(kvec))*PGD_range
PGD_range = PGD_kernel(kvec, kl, ks)
pot_k_pgd = (delta_k * invlaplace_kernel(kvec)) * PGD_range
forces_pgd = jnp.stack([
cic_read(jnp.fft.irfftn(-gradient_kernel(kvec, i) * pot_k_pgd), pos)
for i in range(3)
],
axis=-1)
dpos_pgd = forces_pgd * alpha
forces_pgd= jnp.stack([cic_read(jnp.fft.irfftn(- gradient_kernel(kvec, i)*pot_k_pgd), pos)
for i in range(3)],axis=-1)
dpos_pgd = forces_pgd*alpha
return dpos_pgd
def make_neural_ode_fn(model, mesh_shape):
def neural_nbody_ode(state, a, cosmo:Cosmology, params):
def neural_nbody_ode(state, a, cosmo: Cosmology, params):
"""
state is a tuple (position, velocities)
"""
@ -213,15 +224,19 @@ def make_neural_ode_fn(model, mesh_shape):
delta_k = jnp.fft.rfftn(delta)
# Computes gravitational potential
pot_k = delta_k * invlaplace_kernel(kvec) * longrange_kernel(kvec, r_split=0)
pot_k = delta_k * invlaplace_kernel(kvec) * longrange_kernel(kvec,
r_split=0)
# Apply a correction filter
kk = jnp.sqrt(sum((ki/jnp.pi)**2 for ki in kvec))
pot_k = pot_k *(1. + model.apply(params, kk, jnp.atleast_1d(a)))
kk = jnp.sqrt(sum((ki / jnp.pi)**2 for ki in kvec))
pot_k = pot_k * (1. + model.apply(params, kk, jnp.atleast_1d(a)))
# Computes gravitational forces
forces = jnp.stack([cic_read(jnp.fft.irfftn(- gradient_kernel(kvec, i)*pot_k), pos)
for i in range(3)],axis=-1)
forces = jnp.stack([
cic_read(jnp.fft.irfftn(-gradient_kernel(kvec, i) * pot_k), pos)
for i in range(3)
],
axis=-1)
forces = forces * 1.5 * cosmo.Omega_m
@ -232,4 +247,5 @@ def make_neural_ode_fn(model, mesh_shape):
dvel = 1. / (a**2 * jnp.sqrt(jc.background.Esqr(cosmo, a))) * forces
return dpos, dvel
return neural_nbody_ode